Forearm Motion and Hand Grasp Prediction Based on Target Muscle Bioimpedance for Human-Machine Interaction

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 760 - 769
• Main Authors: Yao, Tianyang, Wu, Yu, Jiang, Dai, Bayford, Richard, Demosthenous, Andreas
• Format: Journal Article
• Language: English
• Published: United States IEEE 2025
• Subjects: Adult; Algorithms; Amputees; Bioimpedance; Biological system modeling; Biomechanical Phenomena; Current measurement; Electric Impedance; Electrical impedance tomography; Electrodes; Female; Forearm - physiology; forearm motion; Grasping; Hand - physiology; hand grasp; Hand Strength - physiology; Hands; human-machine interaction (HMI); Humans; Linear Models; long short-term memory (LSTM); Male; Man-Machine Systems; Movement - physiology; multi-degree of freedom (DoF); Muscle, Skeletal - physiology; Muscles; Regression Analysis; regression model; Reproducibility of Results; robotic control; Support Vector Machine; Thumb; Wrist; Young Adult

This paper introduces a novel methodology for simultaneously predicting hand grasp and forearm motion using target muscle bioimpedance measurements and regression models. A total of six channels, formed by nine electrodes, are employed for this multi-degree of freedom (DoF) prediction. Given the tim...